1. Field of the Invention
The present invention relates to a technology for estimating computing resources in a distributed Internet data center (DIC) system.
2. Description of the Related Art
Recently, plural network services for which operation have been consigned by plural service providers are operated simultaneously in a single data center. A resource allocation control is executed based on a utility scheme in which, among services operated simultaneously, an allocation amount of a computing resource is dynamically increased for a service that has an increasing load, and an allocation amount of a computing resource is dynamically decreased for a service that has a decreasing load (for example, Japanese Patent Application Laid-Open Publication No. 2002-24192).
However, when some of the services operated in a data center are temporarily over-loaded and the amount of resources to be allocated to those services needs to be significantly increased, idle resources to be additionally allocated can lack in the data center and the addition of resources may be impossible.
To prevent such a situation, a peak load in the data center is estimated, and resources are always prepared in an amount necessary at the peak load. The peak load is a peak amount of the total loads of all operation services in the data center. However, in this case, except at the peak load, most of the resources are not used and left to be idle resources. Thus, cost performance and efficiency in use of computing resources are degraded in the data center.
Therefore, in Japanese Patent Application Laid-Open Publication No. 2005-293048, a resource plan creating program is disclosed that creates an optimal disposition plan of a plurality of services to a plurality of data centers to prevent over-loading on the data centers and to improve efficiency of computing resources of the data centers.
According to a resource plan creating program disclosed in Japanese Patent Application Laid-Open Publication No. 2005-293048, when loads of some data centers among data centers connected through a wide area network and linked with each other have respectively reach peak values, a portion of the peak loads of the data centers is transferred to other data centers that have light loads. A service in which the transferred loads are processed is referred to as “derivative service” and a service that is originally operated before the portion of the loads is transferred is referred to as “primitive service”.
Thus, a control scheme in which the amount of loads at the peak time in plural data centers are reduced is disclosed, and optimization of load distribution ratios between primitive services and derivative services at the time of load transfer for each network service is executed.
The resource plan creating program in Japanese Patent Application Laid-Open Publication No. 2005-293048 realizes the optimization of a resource plan by realizing the optimal division of the load in which an optimal percentage of the load at the peak time in an estimation waveform of load fluctuation of a specific network service is cut out, and disposition optimization in which a derivative load cut out is optimally disposed.
FIG. 22 is a schematic for illustrating an effect of an action of the resource plan creating program disclosed in Japanese Patent Application Laid-Open Publication No. 2005-293048. In FIG. 22, graphs 2201 to 2203 are graphs showing waveforms of estimated load fluctuation of data centers D1 to D3 that provide network services A to C before the optimization processes are performed.
Graphs 2211 to 2213 shown in FIG. 22 are graphs showing waveforms of estimated load fluctuation of the data centers D1 to D3 after the optimization processes. In each of the graphs 2201 to 2203 and 2211 to 2213, a horizontal axis represents time. The left end thereof indicates the present and the right end thereof indicates one year later. The vertical axis represents the amount of load.
Before the optimization process, as shown in the graph 2201, the peak value of the load amount of the data center D1 is “F”. After the optimization process, as shown in the graph 2211, a peak portion Pa in the graph 2201 is divided as a derivative load and is transferred to the data center D3 while incorporating a peak portion Pb in the graph 2202 as a derivative load. Thus, the peak amount of the load amount of the data center DC1 is decreased to “f”.
Before the optimization process, as shown in the graph 2202, the peak value of the load amount of the data center D2 is “G”. After the optimization process, as shown in the graph 2212, the peak portion Pb in the graph 2202 is divided as a derivative load and is transferred to the data center D1 while incorporating a peak portion Pc in the graph 2203 as a derivative load. Thus, the peak value of the load amount of the data center D2 is decreased to “g”.
Before the optimization process, as shown in the graph 2203, the peak value of the load amount of the data center D3 is “H”. After the optimization process, as shown in the graph 2213, the peak portion Pc in the graph 2203 is divided as a derivative load and is transferred to the data center D2 while incorporating the peak portion Pa as a derivative load. Thus, the peak value of the load amount of the data center D3 is decreased to “h”.
As shown in FIG. 22, according to the resource plan creating program disclosed in Japanese Patent Application Laid-Open Publication No. 2005-293048, based on the estimation waveform of load fluctuations respectively for network services A to C for one year from now on, optimization of the load distribution ratio between the primitive services and the derivative services of the peak load amount at each time of the peak occurrence when the network services A to C are operated in each of the plurality of data centers D1 to D3 linked with each other.
As described above, according to the resource plan creating program disclosed in Japanese Patent Application Laid-Open Publication No. 2005-293048, optimization of the load distribution ratio is executed for the necessary resource amounts at the peak time of the data centers D1 to D3 such that the peak reduction rate that represents a ratio of a value before and a value after the optimization process becomes as large as possible. Peak reduction rates PRR1 to PRR3 of the data centers D1 to D3 are expressed in the following Equations i to iii.PPR1=(F−f)/F  (i)PPR2=(G−g)/G  (ii)PPR3=(H−h)/H  (iii)
Thus, the amounts of computing resources that the data centers D1 to D3 should respectively prepare in advance can be minimized and the efficiency in use of the computing resources and the cost performance can be improved. In the optimization of this load distribution ratio, it is empirically known that the peak reduction rates PRR1 to PRR3 can be maximized when the load amounts of the data centers D1 to D3 after the optimization are always as equal as possible.
The load distribution ratio obtained as described above at each time of peak occurrence and data centers of optimal destinations to which the derivative services are disposed that are arranged in the time sequence of the peak, based on an estimation waveform of load fluctuation for future one year is referred to as “resource plan”. To obtain a resource plan that maximizes the peak reduction rate is referred to as “optimization of the resource plan”.
According to this resource plan creating program, with the optimization of the resource plan, the prepared amount in a center of the resources in the data center D1 can be reduced to f (<F); the prepared amount in a center of the resources in the data center D2 can be reduced to g (<G); and the prepared amount in a center of the resources in the data center D3 can be reduced to h (<H).
However, in the conventional technique of the above Japanese Patent Application Laid-Open Publication No. 2005-293048, it is assumed that all of the computer hardware in the data centers D1 to D3 is uniform and has no difference in type, and the computing resources demand amount per one transaction of each task (a program that is being executed and that executes functions provided by the network services A to C on the computer hardware) that executes each of the network services A to C in the data center D1 to D3 is uniform.
In practice, in the data centers D1 to D3, various types of computer hardware having different computer hardware architectures, such as a blade server, a high-end server, a symmetric multiple processor (SMP), etc., are present, and the computing resources demand amount per one transaction differs depending on tasks to execute the network services.
Therefore, achievable processing performance may differ between a case where a task is executed on a blade server and a case where the same task is executed on an SMP machine even when the task executes the same information retrieving service and both of the blade server and the SMP machine respectively have the same computer hardware performance values. The necessary computer hardware performance values to achieve the processing performance of one transaction per second may differ depending on the type of task. Therefore, a resource plan optimized without considering such a context can not always be practical, and the plan cannot be applied to the real data centers D1 to D3.
As a result, an operator of the data centers D1 to D3 cannot make any outlook for a long-term equipment investment plan as to how many computing resources in the data centers D1 to D3 should be added at which point in the future, or how much the estimated amount of the equipment procurement cost for the addition will be.
Therefore, making a long-term equipment investment plan that copes with the peak time is necessary. However, most of the computing resources are not used except at the peak time, and the plan results in wasteful equipment investment and increased equipment cost.